2. Experimental evidence for product- oriented generalizations (or not) Vsevolod Kapatsinski Indiana University Dept. of Linguistics Cognitive Science Program Speech Research Laboratory vkapatsi@indiana.edu http://mypage.iu.edu/~vkapatsi/

3. Product-oriented vs. source-oriented generalizations Bybee (2001:126) “Generative rules express source-oriented generalizations. That is, they act on a specific input to change it in well-defined ways into an output of a certain form. Many, if not all, schemas are product- oriented rather than source-oriented. A product- oriented schema generalizes over forms of a specific category, but does not specify how to derive that category from some other.” Source oriented: k] sg  t  i] pl Product-oriented: ‘plurals must end in t  i’

4. Present study Given an artificial lexicon and a particular training paradigm what generalizations do the learners extract?

5. The paradigm (Bybee & Newman 1995)

10. The artificial languages BLUERED {k;g}  {t  ;d  }i 100% 30 {t;d;p;b}  {t;d;p;b}i25% 8 75% 24 {t;d;p;b}  {t;d;p;b}a75% 24 25% 8 Two plural suffixes –i and -a If –i attached to a velar ({k;g}), the velar changes to an alveopalatal This is velar palatalization

11. Velar palatalization The process: k  t  /_i Productivity: p(k  t  i) / ( p(k  t  i) + p(k  ki) ) Coding scheme: BLUE – velar palatalization applies RED – velar palatalization fails

12. Research question Does the productivity of velar palatalization differ in the BLUE language and the RED language? Depends on your model of grammar.

13. The possible grammars BLUERED {k;g}  {t  ;d  }i 100% 30 {t;d;p;b}  {t;d;p;b}i25% 8 75% 24 {t;d;p;b}  {t;d;p;b}a75% 24 25% 8 /62

14. Non-competing rules BLUERED {k;g}  {t  ;d  }i 100% 30 {t;d;p;b}  {t;d;p;b}i25% 8 75% 24 {t;d;p;b}  {t;d;p;b}a75% 24 25% 8 Triggers velar palatalization Does not compete with anything Equally supported in both languages BLUE = RED e.g., Hale and Reiss 2008, Plag 2003

15. Simple positive product- oriented generalizations BLUERED {k;g}  {t  ;d  }i 100% 30 {t;d;p;b}  {t;d;p;b}i25% 8 75% 24 {t;d;p;b}  {t;d;p;b}a75% 24 25% 8 Triggers velar palatalization BLUE = RED Equally supported in both languages Bybee & Slobin 1982, Bybee & Moder 1983, Bybee 2001

16. Negative product-oriented generalizations BLUERED {k;g}  {t  ;d  }i 100% 30 *ki0 {t;d;p;b}  {t;d;p;b}i25% 8 75% 24 Ci3854 {t;d;p;b}  {t;d;p;b}a75% 24 25% 8 Triggers velar palatalization /ki/ less expected in the blue language  its absence is less notable BLUE < RED

17. Competing weighted rules BLUERED {k;g}  {t  ;d  }i 100% 30 C  Ci25% 8 75% 24 C  Ca75% 24 25% 8 Triggers velar palatalization BLUE > RED Competes with Competition stronger in red Albright & Hayes 2003 Iff the choice between the rules is stochastic.

18. Conditional product- oriented generalizations BLUERED {k;g}  {t  ;d  } | i 30/3830/54 {t;d;p;b}  {t;d;p;b} | i8/3824/54 {t;d;p;b}  {t;d;p;b} | a11 BLUE > RED Triggers velar palatalization More reliable in blue Aslin et al. 1998

19. Result BLUE RED

20. Results * 100% 30 BLUE RED Non-competing rules Simple positive product-oriented Negative product-oriented Competing weighted rules Conditional product-oriented

21. Individual subject data

22. Competing weighted rules BLUERED {k;g}  {t  ;d  }i 100% 30 C  Ci25% 8 75% 24 C  Ca75% 24 25% 8 Albright & Hayes 2003 {p;b;t;d}

23. 22 Results *** Competing weighted rules ANCOVA: This correlation is significant F(1,27)=14.23, p<.001, while Language is not, F(1,27)=.082, p>.5). The predicted explanatory variable accounts for all the variance in velar palatalization rate attributable to the artificial language

24. The competing rules look good. Can we pit them against (conditional) product-oriented generalizations directly?

25. BLUERED {k;g}  {t  ;d  } | i Support vel.pal Support vel.pal {t  ;d  }  {t  ;d  } | i {t;d;p;b}  {t;d;p;b} | i8/3824/54 {t;d;p;b}  {t;d;p;b} | a11 Conditional product-oriented generalizations

26. Competing weighted rules BLUERED {k;g}  {t  ;d  }i 100% 30 {t  ;d  }  {t  ;d  }i Oppose vel.pal Oppose vel.pal C  Ci C  Ca75% 24 25% 8

27. Competing weighted rules r(33) = -.49, p=.003

28. The addition of t   t  i hurts vel.pal t(33)=2.88, p=.007 Competing weighted rules (Conditional) product-oriented

29. Something that looks product-oriented but isn’t Result: All subjects attach –i rather than –a to singulars ending in {t  ;d ʒ } In the Blue Language even more than to singulars ending in {k;g} H: Because both languages have plurals ending in {t  ;d ʒ }i, not {t  ;d ʒ }a. A product-oriented schema? ‘Plurals must end in {t  ;d ʒ }i’. BLUE RED

30. If –{t  ;d ʒ }  {t  ;d ʒ }i because of ‘Plurals must end in {t  ;d ʒ }i’, and this is the schema that does {k;g}  {t  ;d ʒ }i, Then –there should be a positive correlation between rate of {k;g}  {t  ;d ʒ }i and rate of {t  ;d ʒ }  {t  ;d ʒ }i But r=-.03, n.s. Why not?

31. It’s categorization of source forms. The more a subject attaches –i to velars, the more s/he attaches it to alveopalatals. Why more –i with {t  ;d ʒ } than with {k;g}? Subjects have a bias against stem changes.

32. Prior experimental evidence for product- oriented generalizations Frequent output patterns get ‘overused’, being derived from inputs in ways that are not attested in the lexicon, e.g., [v  n]  [v  ] (Bybee & Moder, 1983). (Also see Köpcke 1988, Lobben 1991, Wang and Derwing 1994, Albright and Hayes 2003) H: because the subjects have generalized ‘past tense forms must end in [  ]’

33. An alternative interpretation H’: The production of an output primes sublexical chunks occurring in that output. Lobben (1991) –“the plurals [that don’t obey the rules but all end in ooCii] are appearing concentrated and subsequently… and… this is a typical characteristic of all other plural patterns’ (Lobben 1991:173), – ‘[In this example] the second syllable of the singular is left out in the plural form, which never happens with real nouns… The surrounding… plurals, two preceding and seven following… are trisyllabic [in accordance with source-oriented rules]. This… provides an explanation as to why the plural [in this example], which, if produced according to the rule…, would have four syllables, is made to have three syllables in a very unorthodox way’ (Lobben 1991:182) Presupposition: the output is more salient than the input  chunks from the output are more likely to persist and be reused than chunks from the input

34. Are products more salient than sources? During training, subjects repeated the word pairs they heard. Subjects have a bias against stem changes  If the product is more salient, they should tend to erroneously make the shape of the singular fit the shape of the plural.  If the source is more salient, the plural should fit the singular.

35. Products are more salient t   t  i k  ki k  t  i repeated as χ 2 =28.9, p<.0001

36. 35 In this AGL paradigm (Bybee & Newman 1995), Learners extract competing rules The outcome of competition is influenced by reliability or type frequency (Albright and Hayes 2003, Pierrehumbert 2006) The choice between rules is stochastic No evidence for product-oriented generalizations. Future work: Role of the training paradigm. Summary

37. 36 Product forms are more salient than source forms. Thus creating a product may prime chunks and patterns that occur in that product for immediate reuse. This product priming may result in ‘overuse’ of frequent product patterns (found by Bybee & Moder 1983, Koepcke 1988, Lobben 1991, Wang & Derwing 1994, Albright & Hayes, 2003). If long-lasting, it may also result in the emergence of product-oriented schemas over time. Summary

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